In the United States, the commercial weather sector provides forecasts, observations, and other meteorological content to businesses and consumers for direct fees and other forms of compensation. There is, at any time, a strong drive for value-added differentiation in this sector, since most of the basic weather forecast and observational data originates from a freely available, public data source—the National Weather Service (NWS).
Weather information vendors attempt to add value to the basic, public-domain database in many ways to distinguish themselves over other vendors. One area in which value-added products and services are being developed is weather forecasting, and more particularly, very short-term weather forecasting.
Very short-term weather forecasting is generally limited to the first six hours beyond present time (i.e., 0-6 hours). Numerical Weather Prediction (NWP), statistical post-processing of NWP output (e.g., MOS, DMOS, and Perfect Prog), and the human forecaster play important and interrelated roles in the creation of longer range forecasts (i.e., 1-10 days into the future) that are the staple of most public and private weather firms' products and services. In the creation and dissemination of very near term (i.e., 0-6 hours) weather forecasts, however, there are problems with each of these component parts of the forecasting equation.
For example, NWP models simulations often suffer from cold starts or spin-up issues. It is well known and understood that objective Nowcasts via traditional NWP sources are poor because of this spin-up problem. As used herein, Nowcasts is defined as forecasts of what the weather will do in the next few hours and, for the purposes of this application disclosure, can be considered synonymous with the term “very short-term forecasts” or simply “short-term forecasts”.
Statistical post-processes or stand-alone statistical forecasts either suffer from their dependence on NWP output as a starting point, or by their stochastic and non-specific orientation.
The human forecaster is generally good at reacting to and interpreting current weather situations and intuitively extrapolating them into the future, but the very nature of high-resolution (in time and space) short-term forecasts often precludes lengthy analysis and diagnoses because of the short response times necessary to disseminate important Nowcast information.
FIG. 1 is a qualitative depiction of short-term forecast skill in accuracy for different Nowcasting techniques (from Wilson et. al., 1998), and depicts the relationship between forecast skill and time into the future for a number of methods or techniques that may be employed for this purpose. The radar extrapolation or predicative radar is a technique to advect or extrapolate radar reflectivity or echoes into the future based on their current spatial extent and a calculation of the mean direction and speed of movement of existing radar echoes. As shown, this method may be effective in the first ½ to 1 hour, but its skill falls to near zero beyond this time due to issues with propagation, initiation, growth, and decay of precipitating systems.
Conversely, the large scale model (e.g., large-scale NWP) shows little or no skill in the 0-6 hour timeframe, yet its skill or accuracy rises beyond this. One reason for this is that large-scale models suffer from spin-up and as a result have very little skill in the very short-term.
Also, there is a class of NWP called high-resolution (generally below 5 km in the horizontal) explicit models that exhibit better short-range skill based on very sophisticated model initializations of the atmospheric state including explicit treatment of small scale atmospheric processes such as, for example, precipitation processes. Explicit models, however, are still inferior to expert systems or hybrid approaches.
FIG. 2 (from German and Zawadzky, 2002) depicts a second important facet of the very short-term forecasting challenge. In this graph of skill versus time, forecasts are partitioned according to the type of the precipitation rate associated with the system. In this analysis, one can see that precipitating systems exhibiting intense or high precipitation rates are more difficult to predict for appreciable times into the future. Conversely, precipitation systems exhibiting small precipitation rates are more tractable in terms of anticipating their future behavior.
This behavior is most closely linked to the vertical structure of the precipitating system. Those exhibiting intense precipitation rates are most often associated with convective systems of significant vertical extent and limited horizontal extent such as thunderstorms. Systems that have limited or shallow vertical extent but broad horizontal extent are associated with stratiform clouds and lighter precipitation rates. Thus, not surprisingly, there is a distinct dichotomy between the skill of predicting short-lived convective systems much beyond one hour in time—while broader synoptic or stratiform systems can often be skillfully predicted some four, six or more hours into the future.
Thus, in view of the foregoing, there is a need for systems and methods that overcome the limitations and drawbacks of the prior art. In particular, there is a need for systems and methods that provide improved accuracy with respect to conventional single mode methods of very short-term forecasts, such as radar extrapolation, manual human forecasts, and explicitly resolved high-resolution numerical weather predictions. There is strong evidence that a system or method which determines an optimal blend of inputs from such single mode short-term forecasting techniques and dynamically arbitrates between these methods based upon the weather situation or other criteria—would significantly advance the current status of nowcasting accuracy and utility. In the context herewith, an expert system therefore can be defined as a program or system that uses a composite of problem-solving approaches and in due course arbitrates the optimal blend of these approaches based on some internal feedback mechanism or measuring process. Embodiments of the present invention provide such an expert system solution to the weather nowcasting challenge.